Bladder Droplet Notebook
Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.
tissue_of_interest = "Bladder"Load the requisite packages and some additional helper functions.
# read the metadata to get the plates we want
droplet_metadata_filename = here('00_data_ingest', '01_droplet_raw_data', 'metadata_droplet.csv')
droplet_metadata <- read.csv(droplet_metadata_filename, sep=",", header = TRUE)
colnames(droplet_metadata)[1] <- "channel"
droplet_metadataSubset the metadata on the tissue.
tissue_metadata = filter(droplet_metadata, tissue == tissue_of_interest)[,c('channel','tissue','subtissue','mouse.sex', 'mouse.id')]
tissue_metadataUse only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.
# Load the gene names and set the metadata columns by opening the first file
subfolder = paste0(tissue_of_interest, '-', tissue_metadata$channel[1])
raw.data <- Read10X(data.dir = here('00_data_ingest', '01_droplet_raw_data', 'droplet', subfolder))
colnames(raw.data) <- lapply(colnames(raw.data), function(x) paste0(tissue_metadata$channel[1], '_', x))
meta.data = data.frame(row.names = colnames(raw.data))
meta.data['channel'] = tissue_metadata$channel[1]
if (length(tissue_metadata$channel) > 1){
# Some tissues, like Thymus and Heart had only one channel
for(i in 2:nrow(tissue_metadata)){
subfolder = paste0(tissue_of_interest, '-', tissue_metadata$channel[i])
new.data <- Read10X(data.dir = here('00_data_ingest', '01_droplet_raw_data', 'droplet', subfolder))
colnames(new.data) <- lapply(colnames(new.data), function(x) paste0(tissue_metadata$channel[i], '_', x))
new.metadata = data.frame(row.names = colnames(new.data))
new.metadata['channel'] = tissue_metadata$channel[i]
raw.data = cbind(raw.data, new.data)
meta.data = rbind(meta.data, new.metadata)
}
}
rnames = row.names(meta.data)
meta.data <- merge(meta.data, tissue_metadata, sort = F)
row.names(meta.data) <- rnames
dim(raw.data)[1] 23433 2500corner(raw.data)[1] 0 0 0 1 0head(meta.data)Process the raw data and load it into the Seurat object.
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest,
min.cells = 5, min.genes = 5)
tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Create metadata columns for cell_ontology_classs and subcell_ontology_classs
tiss@meta.data[,'cell_ontology_class'] <- NA
tiss@meta.data[,'subcell_ontology_class'] <- NACalculate percent ribosomal genes.
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")A sanity check: genes per cell vs reads per cell.
GenePlot(object = tiss, gene1 = "nUMI", gene2 = "nGene", use.raw=T)
Filter out cells with few reads and few genes.
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nUMI"),
low.thresholds = c(500, 1000), high.thresholds = c(25000, 5000000))Normalize the data, then regress out correlation with total reads
tiss <- NormalizeData(object = tiss)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|tiss <- ScaleData(object = tiss)[1] "Scaling data matrix"
|
| | 0%
|
|====================================================================| 100%tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Run Principal Component Analysis.
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 10The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 0.4
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)
Check expression of genes of interset.
VlnPlot(tiss, genes_to_check)
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7
521 492 491 420 256 190 73 57 Which markers identify a specific cluster?
clust.markers <- FindMarkers(object = tiss, ident.1 = 2, ident.2 = 1, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)print(x = head(x= clust.markers, n = 10))You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 1 % ~01m 32s
|++ | 2 % ~01m 24s
|++ | 3 % ~01m 22s
|+++ | 4 % ~01m 19s
|+++ | 5 % ~01m 17s
|++++ | 6 % ~01m 15s
|++++ | 7 % ~01m 14s
|+++++ | 8 % ~01m 14s
|+++++ | 9 % ~01m 13s
|++++++ | 10% ~01m 12s
|++++++ | 11% ~01m 11s
|+++++++ | 12% ~01m 11s
|+++++++ | 13% ~01m 10s
|++++++++ | 14% ~01m 09s
|++++++++ | 15% ~01m 08s
|+++++++++ | 16% ~01m 07s
|+++++++++ | 17% ~01m 06s
|++++++++++ | 18% ~01m 05s
|++++++++++ | 19% ~01m 04s
|+++++++++++ | 20% ~01m 03s
|+++++++++++ | 21% ~01m 03s
|++++++++++++ | 22% ~01m 02s
|++++++++++++ | 23% ~01m 01s
|+++++++++++++ | 24% ~01m 01s
|+++++++++++++ | 25% ~60s
|++++++++++++++ | 26% ~59s
|++++++++++++++ | 27% ~58s
|+++++++++++++++ | 28% ~57s
|+++++++++++++++ | 29% ~56s
|++++++++++++++++ | 30% ~55s
|++++++++++++++++ | 31% ~55s
|+++++++++++++++++ | 32% ~54s
|+++++++++++++++++ | 33% ~54s
|++++++++++++++++++ | 34% ~53s
|++++++++++++++++++ | 35% ~52s
|+++++++++++++++++++ | 36% ~51s
|+++++++++++++++++++ | 37% ~50s
|++++++++++++++++++++ | 38% ~49s
|++++++++++++++++++++ | 39% ~48s
|+++++++++++++++++++++ | 40% ~48s
|+++++++++++++++++++++ | 41% ~47s
|++++++++++++++++++++++ | 42% ~46s
|++++++++++++++++++++++ | 43% ~45s
|+++++++++++++++++++++++ | 44% ~44s
|+++++++++++++++++++++++ | 45% ~44s
|++++++++++++++++++++++++ | 46% ~43s
|++++++++++++++++++++++++ | 47% ~42s
|+++++++++++++++++++++++++ | 48% ~41s
|+++++++++++++++++++++++++ | 49% ~40s
|++++++++++++++++++++++++++ | 51% ~39s
|++++++++++++++++++++++++++ | 52% ~39s
|+++++++++++++++++++++++++++ | 53% ~38s
|+++++++++++++++++++++++++++ | 54% ~37s
|++++++++++++++++++++++++++++ | 55% ~36s
|++++++++++++++++++++++++++++ | 56% ~36s
|+++++++++++++++++++++++++++++ | 57% ~35s
|+++++++++++++++++++++++++++++ | 58% ~34s
|++++++++++++++++++++++++++++++ | 59% ~33s
|++++++++++++++++++++++++++++++ | 60% ~32s
|+++++++++++++++++++++++++++++++ | 61% ~31s
|+++++++++++++++++++++++++++++++ | 62% ~31s
|++++++++++++++++++++++++++++++++ | 63% ~30s
|++++++++++++++++++++++++++++++++ | 64% ~29s
|+++++++++++++++++++++++++++++++++ | 65% ~28s
|+++++++++++++++++++++++++++++++++ | 66% ~28s
|++++++++++++++++++++++++++++++++++ | 67% ~27s
|++++++++++++++++++++++++++++++++++ | 68% ~26s
|+++++++++++++++++++++++++++++++++++ | 69% ~25s
|+++++++++++++++++++++++++++++++++++ | 70% ~24s
|++++++++++++++++++++++++++++++++++++ | 71% ~24s
|++++++++++++++++++++++++++++++++++++ | 72% ~23s
|+++++++++++++++++++++++++++++++++++++ | 73% ~22s
|+++++++++++++++++++++++++++++++++++++ | 74% ~21s
|++++++++++++++++++++++++++++++++++++++ | 75% ~21s
|++++++++++++++++++++++++++++++++++++++ | 76% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 23s
| | 0 % ~calculating
|+ | 1 % ~01m 30s
|++ | 2 % ~01m 29s
|++ | 3 % ~01m 40s
|+++ | 4 % ~01m 35s
|+++ | 5 % ~01m 30s
|++++ | 6 % ~01m 27s
|++++ | 7 % ~01m 25s
|+++++ | 8 % ~01m 23s
|+++++ | 9 % ~01m 22s
|++++++ | 10% ~01m 21s
|++++++ | 11% ~01m 20s
|+++++++ | 12% ~01m 19s
|+++++++ | 13% ~01m 18s
|++++++++ | 14% ~01m 17s
|++++++++ | 15% ~01m 16s
|+++++++++ | 16% ~01m 15s
|+++++++++ | 18% ~01m 14s
|++++++++++ | 19% ~01m 13s
|++++++++++ | 20% ~01m 12s
|+++++++++++ | 21% ~01m 12s
|+++++++++++ | 22% ~01m 11s
|++++++++++++ | 23% ~01m 10s
|++++++++++++ | 24% ~01m 09s
|+++++++++++++ | 25% ~01m 08s
|+++++++++++++ | 26% ~01m 08s
|++++++++++++++ | 27% ~01m 07s
|++++++++++++++ | 28% ~01m 06s
|+++++++++++++++ | 29% ~01m 05s
|+++++++++++++++ | 30% ~01m 04s
|++++++++++++++++ | 31% ~01m 03s
|++++++++++++++++ | 32% ~01m 02s
|+++++++++++++++++ | 33% ~01m 01s
|++++++++++++++++++ | 34% ~01m 00s
|++++++++++++++++++ | 35% ~59s
|+++++++++++++++++++ | 36% ~58s
|+++++++++++++++++++ | 37% ~57s
|++++++++++++++++++++ | 38% ~56s
|++++++++++++++++++++ | 39% ~55s
|+++++++++++++++++++++ | 40% ~54s
|+++++++++++++++++++++ | 41% ~53s
|++++++++++++++++++++++ | 42% ~52s
|++++++++++++++++++++++ | 43% ~51s
|+++++++++++++++++++++++ | 44% ~50s
|+++++++++++++++++++++++ | 45% ~49s
|++++++++++++++++++++++++ | 46% ~48s
|++++++++++++++++++++++++ | 47% ~47s
|+++++++++++++++++++++++++ | 48% ~46s
|+++++++++++++++++++++++++ | 49% ~46s
|++++++++++++++++++++++++++ | 51% ~45s
|++++++++++++++++++++++++++ | 52% ~44s
|+++++++++++++++++++++++++++ | 53% ~43s
|+++++++++++++++++++++++++++ | 54% ~42s
|++++++++++++++++++++++++++++ | 55% ~41s
|++++++++++++++++++++++++++++ | 56% ~40s
|+++++++++++++++++++++++++++++ | 57% ~39s
|+++++++++++++++++++++++++++++ | 58% ~38s
|++++++++++++++++++++++++++++++ | 59% ~37s
|++++++++++++++++++++++++++++++ | 60% ~36s
|+++++++++++++++++++++++++++++++ | 61% ~35s
|+++++++++++++++++++++++++++++++ | 62% ~34s
|++++++++++++++++++++++++++++++++ | 63% ~33s
|++++++++++++++++++++++++++++++++ | 64% ~32s
|+++++++++++++++++++++++++++++++++ | 65% ~32s
|+++++++++++++++++++++++++++++++++ | 66% ~31s
|++++++++++++++++++++++++++++++++++ | 67% ~30s
|+++++++++++++++++++++++++++++++++++ | 68% ~29s
|+++++++++++++++++++++++++++++++++++ | 69% ~28s
|++++++++++++++++++++++++++++++++++++ | 70% ~27s
|++++++++++++++++++++++++++++++++++++ | 71% ~26s
|+++++++++++++++++++++++++++++++++++++ | 72% ~25s
|+++++++++++++++++++++++++++++++++++++ | 73% ~24s
|++++++++++++++++++++++++++++++++++++++ | 74% ~23s
|++++++++++++++++++++++++++++++++++++++ | 75% ~22s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 29s
| | 0 % ~calculating
|+ | 1 % ~01m 16s
|++ | 2 % ~01m 19s
|++ | 3 % ~01m 17s
|+++ | 4 % ~01m 17s
|+++ | 5 % ~01m 16s
|++++ | 6 % ~01m 15s
|++++ | 7 % ~01m 14s
|+++++ | 8 % ~01m 13s
|+++++ | 9 % ~01m 12s
|++++++ | 10% ~01m 13s
|++++++ | 11% ~01m 12s
|+++++++ | 12% ~01m 11s
|+++++++ | 13% ~01m 11s
|++++++++ | 14% ~01m 10s
|++++++++ | 15% ~01m 09s
|+++++++++ | 16% ~01m 08s
|+++++++++ | 17% ~01m 07s
|++++++++++ | 18% ~01m 06s
|++++++++++ | 19% ~01m 05s
|+++++++++++ | 20% ~01m 05s
|+++++++++++ | 21% ~01m 05s
|++++++++++++ | 22% ~01m 04s
|++++++++++++ | 23% ~01m 03s
|+++++++++++++ | 24% ~01m 02s
|+++++++++++++ | 25% ~01m 01s
|++++++++++++++ | 26% ~01m 00s
|++++++++++++++ | 27% ~59s
|+++++++++++++++ | 28% ~01m 01s
|+++++++++++++++ | 29% ~01m 00s
|++++++++++++++++ | 30% ~59s
|++++++++++++++++ | 31% ~58s
|+++++++++++++++++ | 32% ~57s
|+++++++++++++++++ | 33% ~56s
|++++++++++++++++++ | 34% ~55s
|++++++++++++++++++ | 35% ~54s
|+++++++++++++++++++ | 36% ~53s
|+++++++++++++++++++ | 37% ~52s
|++++++++++++++++++++ | 38% ~52s
|++++++++++++++++++++ | 39% ~51s
|+++++++++++++++++++++ | 40% ~50s
|+++++++++++++++++++++ | 41% ~49s
|++++++++++++++++++++++ | 42% ~48s
|++++++++++++++++++++++ | 43% ~47s
|+++++++++++++++++++++++ | 44% ~46s
|+++++++++++++++++++++++ | 45% ~46s
|++++++++++++++++++++++++ | 46% ~45s
|++++++++++++++++++++++++ | 47% ~44s
|+++++++++++++++++++++++++ | 48% ~43s
|+++++++++++++++++++++++++ | 49% ~42s
|++++++++++++++++++++++++++ | 51% ~42s
|++++++++++++++++++++++++++ | 52% ~41s
|+++++++++++++++++++++++++++ | 53% ~40s
|+++++++++++++++++++++++++++ | 54% ~39s
|++++++++++++++++++++++++++++ | 55% ~38s
|++++++++++++++++++++++++++++ | 56% ~38s
|+++++++++++++++++++++++++++++ | 57% ~37s
|+++++++++++++++++++++++++++++ | 58% ~36s
|++++++++++++++++++++++++++++++ | 59% ~35s
|++++++++++++++++++++++++++++++ | 60% ~34s
|+++++++++++++++++++++++++++++++ | 61% ~33s
|+++++++++++++++++++++++++++++++ | 62% ~32s
|++++++++++++++++++++++++++++++++ | 63% ~31s
|++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 65% ~30s
|+++++++++++++++++++++++++++++++++ | 66% ~29s
|++++++++++++++++++++++++++++++++++ | 67% ~28s
|++++++++++++++++++++++++++++++++++ | 68% ~27s
|+++++++++++++++++++++++++++++++++++ | 69% ~26s
|+++++++++++++++++++++++++++++++++++ | 70% ~25s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|+++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 75% ~21s
|++++++++++++++++++++++++++++++++++++++ | 76% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 24s
| | 0 % ~calculating
|+ | 1 % ~01m 26s
|++ | 2 % ~01m 23s
|++ | 3 % ~01m 23s
|+++ | 4 % ~01m 22s
|+++ | 5 % ~01m 30s
|++++ | 6 % ~01m 27s
|++++ | 7 % ~01m 25s
|+++++ | 9 % ~01m 23s
|+++++ | 10% ~01m 22s
|++++++ | 11% ~01m 20s
|++++++ | 12% ~01m 19s
|+++++++ | 13% ~01m 18s
|+++++++ | 14% ~01m 17s
|++++++++ | 15% ~01m 15s
|++++++++ | 16% ~01m 14s
|+++++++++ | 17% ~01m 13s
|++++++++++ | 18% ~01m 13s
|++++++++++ | 19% ~01m 11s
|+++++++++++ | 20% ~01m 10s
|+++++++++++ | 21% ~01m 09s
|++++++++++++ | 22% ~01m 08s
|++++++++++++ | 23% ~01m 07s
|+++++++++++++ | 24% ~01m 06s
|+++++++++++++ | 26% ~01m 05s
|++++++++++++++ | 27% ~01m 04s
|++++++++++++++ | 28% ~01m 03s
|+++++++++++++++ | 29% ~01m 02s
|+++++++++++++++ | 30% ~01m 01s
|++++++++++++++++ | 31% ~01m 00s
|++++++++++++++++ | 32% ~59s
|+++++++++++++++++ | 33% ~58s
|++++++++++++++++++ | 34% ~57s
|++++++++++++++++++ | 35% ~57s
|+++++++++++++++++++ | 36% ~56s
|+++++++++++++++++++ | 37% ~55s
|++++++++++++++++++++ | 38% ~54s
|++++++++++++++++++++ | 39% ~53s
|+++++++++++++++++++++ | 40% ~52s
|+++++++++++++++++++++ | 41% ~51s
|++++++++++++++++++++++ | 43% ~50s
|++++++++++++++++++++++ | 44% ~49s
|+++++++++++++++++++++++ | 45% ~48s
|+++++++++++++++++++++++ | 46% ~47s
|++++++++++++++++++++++++ | 47% ~47s
|++++++++++++++++++++++++ | 48% ~46s
|+++++++++++++++++++++++++ | 49% ~46s
|+++++++++++++++++++++++++ | 50% ~45s
|++++++++++++++++++++++++++ | 51% ~44s
|+++++++++++++++++++++++++++ | 52% ~43s
|+++++++++++++++++++++++++++ | 53% ~42s
|++++++++++++++++++++++++++++ | 54% ~41s
|++++++++++++++++++++++++++++ | 55% ~40s
|+++++++++++++++++++++++++++++ | 56% ~39s
|+++++++++++++++++++++++++++++ | 57% ~38s
|++++++++++++++++++++++++++++++ | 59% ~37s
|++++++++++++++++++++++++++++++ | 60% ~36s
|+++++++++++++++++++++++++++++++ | 61% ~35s
|+++++++++++++++++++++++++++++++ | 62% ~34s
|++++++++++++++++++++++++++++++++ | 63% ~33s
|++++++++++++++++++++++++++++++++ | 64% ~32s
|+++++++++++++++++++++++++++++++++ | 65% ~31s
|+++++++++++++++++++++++++++++++++ | 66% ~30s
|++++++++++++++++++++++++++++++++++ | 67% ~29s
|+++++++++++++++++++++++++++++++++++ | 68% ~28s
|+++++++++++++++++++++++++++++++++++ | 69% ~27s
|++++++++++++++++++++++++++++++++++++ | 70% ~26s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|+++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|++++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 76% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 26s
| | 0 % ~calculating
|+ | 1 % ~01m 23s
|++ | 2 % ~01m 22s
|++ | 3 % ~01m 21s
|+++ | 4 % ~01m 21s
|+++ | 5 % ~01m 21s
|++++ | 6 % ~01m 21s
|++++ | 7 % ~01m 20s
|+++++ | 8 % ~01m 19s
|+++++ | 9 % ~01m 18s
|++++++ | 11% ~01m 18s
|++++++ | 12% ~01m 17s
|+++++++ | 13% ~01m 17s
|+++++++ | 14% ~01m 16s
|++++++++ | 15% ~01m 15s
|++++++++ | 16% ~01m 14s
|+++++++++ | 17% ~01m 13s
|+++++++++ | 18% ~01m 12s
|++++++++++ | 19% ~01m 11s
|++++++++++ | 20% ~01m 10s
|+++++++++++ | 21% ~01m 09s
|++++++++++++ | 22% ~01m 08s
|++++++++++++ | 23% ~01m 07s
|+++++++++++++ | 24% ~01m 06s
|+++++++++++++ | 25% ~01m 05s
|++++++++++++++ | 26% ~01m 04s
|++++++++++++++ | 27% ~01m 03s
|+++++++++++++++ | 28% ~01m 02s
|+++++++++++++++ | 29% ~01m 02s
|++++++++++++++++ | 31% ~01m 01s
|++++++++++++++++ | 32% ~60s
|+++++++++++++++++ | 33% ~59s
|+++++++++++++++++ | 34% ~58s
|++++++++++++++++++ | 35% ~57s
|++++++++++++++++++ | 36% ~56s
|+++++++++++++++++++ | 37% ~55s
|+++++++++++++++++++ | 38% ~54s
|++++++++++++++++++++ | 39% ~53s
|++++++++++++++++++++ | 40% ~53s
|+++++++++++++++++++++ | 41% ~52s
|++++++++++++++++++++++ | 42% ~51s
|++++++++++++++++++++++ | 43% ~50s
|+++++++++++++++++++++++ | 44% ~49s
|+++++++++++++++++++++++ | 45% ~48s
|++++++++++++++++++++++++ | 46% ~47s
|++++++++++++++++++++++++ | 47% ~47s
|+++++++++++++++++++++++++ | 48% ~46s
|+++++++++++++++++++++++++ | 49% ~45s
|++++++++++++++++++++++++++ | 51% ~44s
|++++++++++++++++++++++++++ | 52% ~43s
|+++++++++++++++++++++++++++ | 53% ~42s
|+++++++++++++++++++++++++++ | 54% ~41s
|++++++++++++++++++++++++++++ | 55% ~40s
|++++++++++++++++++++++++++++ | 56% ~39s
|+++++++++++++++++++++++++++++ | 57% ~38s
|+++++++++++++++++++++++++++++ | 58% ~38s
|++++++++++++++++++++++++++++++ | 59% ~37s
|++++++++++++++++++++++++++++++ | 60% ~36s
|+++++++++++++++++++++++++++++++ | 61% ~35s
|++++++++++++++++++++++++++++++++ | 62% ~34s
|++++++++++++++++++++++++++++++++ | 63% ~33s
|+++++++++++++++++++++++++++++++++ | 64% ~32s
|+++++++++++++++++++++++++++++++++ | 65% ~31s
|++++++++++++++++++++++++++++++++++ | 66% ~30s
|++++++++++++++++++++++++++++++++++ | 67% ~29s
|+++++++++++++++++++++++++++++++++++ | 68% ~28s
|+++++++++++++++++++++++++++++++++++ | 69% ~27s
|++++++++++++++++++++++++++++++++++++ | 71% ~26s
|++++++++++++++++++++++++++++++++++++ | 72% ~25s
|+++++++++++++++++++++++++++++++++++++ | 73% ~24s
|+++++++++++++++++++++++++++++++++++++ | 74% ~24s
|++++++++++++++++++++++++++++++++++++++ | 75% ~23s
|++++++++++++++++++++++++++++++++++++++ | 76% ~22s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 29s
| | 0 % ~calculating
|+ | 1 % ~01m 26s
|++ | 2 % ~01m 26s
|++ | 3 % ~01m 24s
|+++ | 4 % ~01m 23s
|+++ | 5 % ~01m 22s
|++++ | 6 % ~01m 21s
|++++ | 7 % ~01m 24s
|+++++ | 9 % ~01m 22s
|+++++ | 10% ~01m 21s
|++++++ | 11% ~01m 20s
|++++++ | 12% ~01m 19s
|+++++++ | 13% ~01m 18s
|+++++++ | 14% ~01m 17s
|++++++++ | 15% ~01m 16s
|++++++++ | 16% ~01m 15s
|+++++++++ | 17% ~01m 14s
|++++++++++ | 18% ~01m 12s
|++++++++++ | 19% ~01m 11s
|+++++++++++ | 20% ~01m 10s
|+++++++++++ | 21% ~01m 09s
|++++++++++++ | 22% ~01m 08s
|++++++++++++ | 23% ~01m 07s
|+++++++++++++ | 24% ~01m 06s
|+++++++++++++ | 26% ~01m 05s
|++++++++++++++ | 27% ~01m 04s
|++++++++++++++ | 28% ~01m 03s
|+++++++++++++++ | 29% ~01m 02s
|+++++++++++++++ | 30% ~01m 01s
|++++++++++++++++ | 31% ~59s
|++++++++++++++++ | 32% ~58s
|+++++++++++++++++ | 33% ~57s
|++++++++++++++++++ | 34% ~56s
|++++++++++++++++++ | 35% ~55s
|+++++++++++++++++++ | 36% ~55s
|+++++++++++++++++++ | 37% ~54s
|++++++++++++++++++++ | 38% ~53s
|++++++++++++++++++++ | 39% ~52s
|+++++++++++++++++++++ | 40% ~51s
|+++++++++++++++++++++ | 41% ~50s
|++++++++++++++++++++++ | 43% ~49s
|++++++++++++++++++++++ | 44% ~48s
|+++++++++++++++++++++++ | 45% ~47s
|+++++++++++++++++++++++ | 46% ~46s
|++++++++++++++++++++++++ | 47% ~45s
|++++++++++++++++++++++++ | 48% ~44s
|+++++++++++++++++++++++++ | 49% ~43s
|+++++++++++++++++++++++++ | 50% ~42s
|++++++++++++++++++++++++++ | 51% ~41s
|+++++++++++++++++++++++++++ | 52% ~40s
|+++++++++++++++++++++++++++ | 53% ~40s
|++++++++++++++++++++++++++++ | 54% ~39s
|++++++++++++++++++++++++++++ | 55% ~38s
|+++++++++++++++++++++++++++++ | 56% ~37s
|+++++++++++++++++++++++++++++ | 57% ~36s
|++++++++++++++++++++++++++++++ | 59% ~35s
|++++++++++++++++++++++++++++++ | 60% ~35s
|+++++++++++++++++++++++++++++++ | 61% ~34s
|+++++++++++++++++++++++++++++++ | 62% ~33s
|++++++++++++++++++++++++++++++++ | 63% ~32s
|++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 65% ~30s
|+++++++++++++++++++++++++++++++++ | 66% ~29s
|++++++++++++++++++++++++++++++++++ | 67% ~28s
|+++++++++++++++++++++++++++++++++++ | 68% ~27s
|+++++++++++++++++++++++++++++++++++ | 69% ~26s
|++++++++++++++++++++++++++++++++++++ | 70% ~25s
|++++++++++++++++++++++++++++++++++++ | 71% ~24s
|+++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|++++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 76% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 24s
| | 0 % ~calculating
|+ | 1 % ~01m 48s
|++ | 2 % ~01m 46s
|++ | 3 % ~01m 45s
|+++ | 4 % ~01m 43s
|+++ | 5 % ~01m 42s
|++++ | 6 % ~01m 40s
|++++ | 7 % ~01m 39s
|+++++ | 8 % ~01m 38s
|+++++ | 9 % ~01m 38s
|++++++ | 10% ~01m 37s
|++++++ | 11% ~01m 36s
|+++++++ | 12% ~01m 35s
|+++++++ | 13% ~01m 34s
|++++++++ | 14% ~01m 33s
|++++++++ | 15% ~01m 31s
|+++++++++ | 16% ~01m 30s
|+++++++++ | 18% ~01m 29s
|++++++++++ | 19% ~01m 28s
|++++++++++ | 20% ~01m 27s
|+++++++++++ | 21% ~01m 25s
|+++++++++++ | 22% ~01m 24s
|++++++++++++ | 23% ~01m 23s
|++++++++++++ | 24% ~01m 22s
|+++++++++++++ | 25% ~01m 21s
|+++++++++++++ | 26% ~01m 20s
|++++++++++++++ | 27% ~01m 19s
|++++++++++++++ | 28% ~01m 18s
|+++++++++++++++ | 29% ~01m 16s
|+++++++++++++++ | 30% ~01m 15s
|++++++++++++++++ | 31% ~01m 14s
|++++++++++++++++ | 32% ~01m 13s
|+++++++++++++++++ | 33% ~01m 12s
|++++++++++++++++++ | 34% ~01m 11s
|++++++++++++++++++ | 35% ~01m 10s
|+++++++++++++++++++ | 36% ~01m 09s
|+++++++++++++++++++ | 37% ~01m 08s
|++++++++++++++++++++ | 38% ~01m 07s
|++++++++++++++++++++ | 39% ~01m 06s
|+++++++++++++++++++++ | 40% ~01m 05s
|+++++++++++++++++++++ | 41% ~01m 04s
|++++++++++++++++++++++ | 42% ~01m 03s
|++++++++++++++++++++++ | 43% ~01m 02s
|+++++++++++++++++++++++ | 44% ~01m 01s
|+++++++++++++++++++++++ | 45% ~59s
|++++++++++++++++++++++++ | 46% ~58s
|++++++++++++++++++++++++ | 47% ~57s
|+++++++++++++++++++++++++ | 48% ~56s
|+++++++++++++++++++++++++ | 49% ~55s
|++++++++++++++++++++++++++ | 51% ~54s
|++++++++++++++++++++++++++ | 52% ~53s
|+++++++++++++++++++++++++++ | 53% ~51s
|+++++++++++++++++++++++++++ | 54% ~50s
|++++++++++++++++++++++++++++ | 55% ~49s
|++++++++++++++++++++++++++++ | 56% ~48s
|+++++++++++++++++++++++++++++ | 57% ~47s
|+++++++++++++++++++++++++++++ | 58% ~46s
|++++++++++++++++++++++++++++++ | 59% ~45s
|++++++++++++++++++++++++++++++ | 60% ~44s
|+++++++++++++++++++++++++++++++ | 61% ~43s
|+++++++++++++++++++++++++++++++ | 62% ~42s
|++++++++++++++++++++++++++++++++ | 63% ~41s
|++++++++++++++++++++++++++++++++ | 64% ~40s
|+++++++++++++++++++++++++++++++++ | 65% ~39s
|+++++++++++++++++++++++++++++++++ | 66% ~38s
|++++++++++++++++++++++++++++++++++ | 67% ~37s
|+++++++++++++++++++++++++++++++++++ | 68% ~35s
|+++++++++++++++++++++++++++++++++++ | 69% ~34s
|++++++++++++++++++++++++++++++++++++ | 70% ~33s
|++++++++++++++++++++++++++++++++++++ | 71% ~32s
|+++++++++++++++++++++++++++++++++++++ | 72% ~31s
|+++++++++++++++++++++++++++++++++++++ | 73% ~30s
|++++++++++++++++++++++++++++++++++++++ | 74% ~29s
|++++++++++++++++++++++++++++++++++++++ | 75% ~27s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~23s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~21s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 51s
| | 0 % ~calculating
|+ | 1 % ~02m 07s
|++ | 2 % ~02m 04s
|++ | 3 % ~02m 03s
|+++ | 4 % ~02m 02s
|+++ | 5 % ~02m 00s
|++++ | 6 % ~01m 59s
|++++ | 7 % ~01m 57s
|+++++ | 8 % ~01m 56s
|+++++ | 9 % ~01m 55s
|++++++ | 10% ~01m 54s
|++++++ | 11% ~01m 54s
|+++++++ | 12% ~01m 52s
|+++++++ | 13% ~01m 51s
|++++++++ | 14% ~01m 49s
|++++++++ | 15% ~01m 48s
|+++++++++ | 16% ~01m 47s
|+++++++++ | 17% ~01m 45s
|++++++++++ | 18% ~01m 44s
|++++++++++ | 19% ~01m 43s
|+++++++++++ | 20% ~01m 42s
|+++++++++++ | 21% ~01m 41s
|++++++++++++ | 22% ~01m 40s
|++++++++++++ | 23% ~01m 38s
|+++++++++++++ | 24% ~01m 37s
|+++++++++++++ | 26% ~01m 36s
|++++++++++++++ | 27% ~01m 35s
|++++++++++++++ | 28% ~01m 34s
|+++++++++++++++ | 29% ~01m 33s
|+++++++++++++++ | 30% ~01m 31s
|++++++++++++++++ | 31% ~01m 30s
|++++++++++++++++ | 32% ~01m 29s
|+++++++++++++++++ | 33% ~01m 27s
|+++++++++++++++++ | 34% ~01m 26s
|++++++++++++++++++ | 35% ~01m 24s
|++++++++++++++++++ | 36% ~01m 23s
|+++++++++++++++++++ | 37% ~01m 21s
|+++++++++++++++++++ | 38% ~01m 20s
|++++++++++++++++++++ | 39% ~01m 19s
|++++++++++++++++++++ | 40% ~01m 18s
|+++++++++++++++++++++ | 41% ~01m 16s
|+++++++++++++++++++++ | 42% ~01m 15s
|++++++++++++++++++++++ | 43% ~01m 14s
|++++++++++++++++++++++ | 44% ~01m 12s
|+++++++++++++++++++++++ | 45% ~01m 11s
|+++++++++++++++++++++++ | 46% ~01m 10s
|++++++++++++++++++++++++ | 47% ~01m 08s
|++++++++++++++++++++++++ | 48% ~01m 07s
|+++++++++++++++++++++++++ | 49% ~01m 06s
|+++++++++++++++++++++++++ | 50% ~01m 04s
|++++++++++++++++++++++++++ | 51% ~01m 03s
|+++++++++++++++++++++++++++ | 52% ~01m 02s
|+++++++++++++++++++++++++++ | 53% ~01m 01s
|++++++++++++++++++++++++++++ | 54% ~59s
|++++++++++++++++++++++++++++ | 55% ~58s
|+++++++++++++++++++++++++++++ | 56% ~57s
|+++++++++++++++++++++++++++++ | 57% ~55s
|++++++++++++++++++++++++++++++ | 58% ~54s
|++++++++++++++++++++++++++++++ | 59% ~53s
|+++++++++++++++++++++++++++++++ | 60% ~52s
|+++++++++++++++++++++++++++++++ | 61% ~50s
|++++++++++++++++++++++++++++++++ | 62% ~49s
|++++++++++++++++++++++++++++++++ | 63% ~48s
|+++++++++++++++++++++++++++++++++ | 64% ~46s
|+++++++++++++++++++++++++++++++++ | 65% ~45s
|++++++++++++++++++++++++++++++++++ | 66% ~44s
|++++++++++++++++++++++++++++++++++ | 67% ~43s
|+++++++++++++++++++++++++++++++++++ | 68% ~41s
|+++++++++++++++++++++++++++++++++++ | 69% ~40s
|++++++++++++++++++++++++++++++++++++ | 70% ~39s
|++++++++++++++++++++++++++++++++++++ | 71% ~37s
|+++++++++++++++++++++++++++++++++++++ | 72% ~36s
|+++++++++++++++++++++++++++++++++++++ | 73% ~34s
|++++++++++++++++++++++++++++++++++++++ | 74% ~33s
|++++++++++++++++++++++++++++++++++++++ | 76% ~32s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~30s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~29s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~28s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~26s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~25s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~24s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~21s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~18s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 08ssave.image("bladder_droplet_image.RData")Display the top markers you computed above.
tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
tiss= BuildClusterTree(tiss)[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"
[1] "Finished averaging RNA for cluster 6"
[1] "Finished averaging RNA for cluster 7"
Node11_markers = FindAllMarkersNode(tiss, node = 11)
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~05s
|+++ | 5 % ~05s
|+++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~05s
|++++++ | 10% ~05s
|++++++ | 12% ~05s
|+++++++ | 13% ~05s
|+++++++ | 14% ~05s
|++++++++ | 15% ~05s
|+++++++++ | 16% ~05s
|+++++++++ | 17% ~04s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|++++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 33% ~04s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|++++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 05s
| | 0 % ~calculating
|+ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|++++++++ | 14% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02sNode11_markers %>% group_by(cluster) %>% top_n(30, avg_logFC)Assigning cell type identity to clusters
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
free.cell_ontology_class <- c("Bladder mesenchymal cell", "Bladder mesenchymal cell", "Basal bladder epithelial cell", "Luminal bladder epithelial cell", "Luminal bladder epithelial cell", "Bladder mesenchymal cell", "Monocyte/Macrophage", "Endothelial cell")
cell_ontology_class <- c("bladder cell", "bladder cell" , "bladder urothelial cell", "bladder urothelial cell", "bladder urothelial cell", "bladder cell", "leukocyte", "endothelial cell")
in_cell_ontology = sapply(cell_ontology_class, function(x) is.element(x, cell_ontology$name))
if (!all(in_cell_ontology)) {
message = paste0('"', cell_ontology_class[!in_cell_ontology], '" is not in the cell ontology\n')
stop(message)
}
cell_ontology_id <- c("CL:1001319", "CL:1001319" , "CL_1001428", "CL_1001428", "CL_1001428", "CL:1001319", "CL_0000738", "CL_0000115")
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "channel")
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$channel))
10X_P4_3 10X_P4_4 10X_P7_7
0 52 398 71
1 31 461 0
2 36 280 175
3 3 398 19
4 18 171 67
5 2 0 188
6 4 49 20
7 3 26 28# Subset data based on cluster id
subtiss <- SubsetData(object = tiss, ident.use = c(3), do.center = F, do.scale = F, cells.use = )
# To subset data based on annotation or other metadata, you can explicitly pass cell names
cells.to.use = tiss@cell.names[which(tiss@meta.data$mouse.sex == 'F')]
subtiss <- SubsetData(object = tiss, cells.use = cells.to.use, do.center = F, do.scale = F)Save the Robject for later
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)[1] "/Users/Aaron/Documents/GitHub/tabula-muris/00_data_ingest/04_tissue_robj_generated/dropletBladder_seurat_tiss.Robj"save(tiss, file=filename)Export the final metadata
So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.
head(tiss@meta.data)filename = here('00_data_ingest', '03_tissue_cell_ontology_class_csv',
paste0(tissue_of_interest, "_droplet_cell_ontology_class.csv"))
write.csv(tiss@meta.data[,c('channel','cell_ontology_class','cell_ontology_id')], file=filename)cannot open file '/Users/Aaron/Documents/GitHub/tabula-muris/00_data_ingest/03_tissue_cell_ontology_class_csv/Bladder_droplet_cell_ontology_class.csv': No such file or directoryError in file(file, ifelse(append, "a", "w")) :
cannot open the connectionno analysis completed below
Subset and iterate
We can repeat the above analysis on a subset of cells, defined using cluster IDs or some other metadata. This is a good way to drill down and find substructure.
First subset
# If this fails for your subset, it may be that cells.use is more cells than you have left! Try reducing it.
PCHeatmap(object = subtiss, pc.use = 1:3, cells.use = 250, do.balanced = TRUE, label.columns = FALSE, num.genes = 12)
subtiss <- NormalizeData(object = subtiss)
subtiss <- ScaleData(object = subtiss, vars.to.regress = c("nUMI", "percent.ribo","Rn45s"))Run Principal Component Analysis.
# Set number of principal components.
sub.n.pcs = 5# If this fails for your subset, it may be that cells.use is more cells than you have left! Try reducing it.
PCHeatmap(object = subtiss, pc.use = 1:3, cells.use = 250, do.balanced = TRUE, label.columns = FALSE, num.genes = 12)Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
Choose the number of principal components to use.
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
subtiss <- RunTSNE(object = subtiss, dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=20)The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
# Set resolution
sub.res.used <- 1
subtiss <- FindClusters(object = subtiss, reduction.type = "pca", dims.use = 1:sub.n.pcs,
resolution = sub.res.used, ,print.output = 0, save.SNN = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
subtiss <- RunTSNE(object = subtiss, dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=20)# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = subtiss, do.label = T)# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(subtiss, genes_to_check, plot.legend = T)
subtiss.markers %>% group_by(cluster) %>% top_n(6, avg_diff)Check expression of genes of interset.
genes_to_check = c('Alb', 'Cyp2f2', 'Cyp2e1', 'Hamp', 'Glul', 'Ass1', 'Axin2', 'Igfbp2')
FeaturePlot(subtiss, genes_to_check, pt.size = 1)Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
table(as.character(subtiss@ident), as.character(subtiss@meta.data$channel))
10X_P7_7
0 167
1 117
2 93
3 74
4 69
5 35
6 13How big are the clusters?
table(subtiss@ident)Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = subtiss, do.return = TRUE, group.by = "channel")Print a table showing the count of cells in each identity category from each plate.
Save the Robject for later
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)# To reload a saved object
# filename = here('00_data_ingest', '04_tissue_robj_generated',
# paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
# load(file=filename)Export the final metadata
So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.
head(tiss@meta.data)filename = here('00_data_ingest', '03_tissue_cell_ontology_class_csv',
paste0(tissue_of_interest, "_droplet_cell_ontology_class.csv"))
write.csv(tiss@meta.data[,c('channel','cell_ontology_class','cell_ontology_id')], file=filename)---
 title: "Bladder Droplet Notebook"
 output: html_notebook
---

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

```{r}
tissue_of_interest = "Bladder"
```

Load the requisite packages and some additional helper functions.

```{r}
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
library(ontologyIndex)
cell_ontology = get_ontology('https://raw.githubusercontent.com/obophenotype/cell-ontology/master/cl-basic.obo', extract_tags='everything')

validate_cell_ontology = function(cell_ontology_class){
  in_cell_ontology = sapply(cell_ontology_class, function(x) is.element(x, cell_ontology$name))
  if (!all(in_cell_ontology)) {
    message = paste0('"', cell_ontology_class[!in_cell_ontology], '" is not in the cell ontology
')
    stop(message)
  }
}
convert_to_cell_ontology_id = function(cell_ontology_class){
  return(sapply(cell_ontology_class, function(x) as.vector(cell_ontology$id[cell_ontology$name == x])[1]))
}
save_dir = here('00_data_ingest', '04_tissue_robj_generated')
```



```{r}
# read the metadata to get the plates we want
droplet_metadata_filename = here('00_data_ingest', '01_droplet_raw_data', 'metadata_droplet.csv')

droplet_metadata <- read.csv(droplet_metadata_filename, sep=",", header = TRUE)
colnames(droplet_metadata)[1] <- "channel"
droplet_metadata
```

Subset the metadata on the tissue.

```{r}
tissue_metadata = filter(droplet_metadata, tissue == tissue_of_interest)[,c('channel','tissue','subtissue','mouse.sex', 'mouse.id')]
tissue_metadata
```


Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to "expand" the per-plate metadata to be per-cell.

```{r}
# Load the gene names and set the metadata columns by opening the first file

subfolder = paste0(tissue_of_interest, '-', tissue_metadata$channel[1])
raw.data <- Read10X(data.dir = here('00_data_ingest', '01_droplet_raw_data', 'droplet', subfolder))
colnames(raw.data) <- lapply(colnames(raw.data), function(x) paste0(tissue_metadata$channel[1], '_', x))
meta.data = data.frame(row.names = colnames(raw.data))
meta.data['channel'] = tissue_metadata$channel[1]

if (length(tissue_metadata$channel) > 1){
  # Some tissues, like Thymus and Heart had only one channel
  for(i in 2:nrow(tissue_metadata)){
    subfolder = paste0(tissue_of_interest, '-', tissue_metadata$channel[i])
    new.data <- Read10X(data.dir = here('00_data_ingest', '01_droplet_raw_data', 'droplet', subfolder))
    colnames(new.data) <- lapply(colnames(new.data), function(x) paste0(tissue_metadata$channel[i], '_', x))
    
    new.metadata = data.frame(row.names = colnames(new.data))
    new.metadata['channel'] = tissue_metadata$channel[i]
    
    raw.data = cbind(raw.data, new.data)
    meta.data = rbind(meta.data, new.metadata)
  }
}

rnames = row.names(meta.data)
meta.data <- merge(meta.data, tissue_metadata, sort = F)
row.names(meta.data) <- rnames
dim(raw.data)
corner(raw.data)
head(meta.data)

```

Process the raw data and load it into the Seurat object.

```{r}
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)

tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")

# Create metadata columns for cell_ontology_classs and subcell_ontology_classs
tiss@meta.data[,'cell_ontology_class'] <- NA
tiss@meta.data[,'subcell_ontology_class'] <- NA
```


Calculate percent ribosomal genes.

```{r}
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")
```

A sanity check: genes per cell vs reads per cell.

```{r}
GenePlot(object = tiss, gene1 = "nUMI", gene2 = "nGene", use.raw=T)
```

Filter out cells with few reads and few genes.

```{r}
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nUMI"), 
    low.thresholds = c(500, 1000), high.thresholds = c(25000, 5000000))
```


Normalize the data, then regress out correlation with total reads
```{r}
tiss <- NormalizeData(object = tiss)
tiss <- ScaleData(object = tiss)
tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
```


Run Principal Component Analysis.
```{r}
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
```

```{r, echo=FALSE, fig.height=4, fig.width=8}
PCHeatmap(object = tiss, pc.use = 1:3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = tiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
n.pcs = 10
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

```{r}
# Set resolution 
res.used <- 0.4

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
```


To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)
```

```{r}
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)
```

Check expression of genes of interset.

```{r, echo=FALSE, fig.height=12, fig.width=8}
genes_to_check = c('Epcam','Upk1b', 'Upk3a','Grhl3', 'Krt5', 'Krt14', 'Dcn', 'Col1a1','Ptch1', 'Hhip', 'Pecam1', 'Cd14', 'Scara5', 'Car3')
#genes_to_check = c('Alb', 'Cyp2f2', 'Cyp2, 'Krt20')

FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3)
```

```{r}
VlnPlot(tiss, genes_to_check)
```


Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r, echo=FALSE, fig.height=4, fig.width=8}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```

How big are the clusters?
```{r}
table(tiss@ident)
```


Which markers identify a specific cluster?

```{r}
clust.markers <- FindMarkers(object = tiss, ident.1 = 2, ident.2 = 1, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```


```{r}
print(x = head(x= clust.markers, n = 10))
```

You can also compute all markers for all clusters at once. This may take some time.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

```{r}
save.image("bladder_droplet_image.RData")
```


Display the top markers you computed above.
```{r}
tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
```

```{r, echo=FALSE, fig.height=4, fig.width=8}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```

```{r}
tiss= BuildClusterTree(tiss)
```

```{r}
Node11_markers = FindAllMarkersNode(tiss, node = 11)
```

```{r}
Node11_markers %>% group_by(cluster) %>% top_n(30, avg_logFC)
```


## Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:


```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
free.cell_ontology_class <- c("Bladder mesenchymal cell", "Bladder mesenchymal cell", "Basal bladder epithelial cell", "Luminal bladder epithelial cell", "Luminal bladder epithelial cell", "Bladder mesenchymal cell", "Monocyte/Macrophage", "Endothelial cell")

cell_ontology_class <- c("bladder cell", "bladder cell" , "bladder urothelial cell", "bladder urothelial cell", "bladder urothelial cell", "bladder cell", "leukocyte", "endothelial cell")
in_cell_ontology = sapply(cell_ontology_class, function(x) is.element(x, cell_ontology$name))
if (!all(in_cell_ontology)) {
  message = paste0('"', cell_ontology_class[!in_cell_ontology], '" is not in the cell ontology\n')
  stop(message)
}
cell_ontology_id <- c("CL:1001319", "CL:1001319" , "CL_1001428", "CL_1001428", "CL_1001428", "CL:1001319", "CL_0000738", "CL_0000115")

tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)

TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
```


## Checking for batch effects


Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "channel")
```

```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$channel))
```

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$mouse.id))
```

# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                    paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```

# Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', '03_tissue_cell_ontology_class_csv', 
                    paste0(tissue_of_interest, "_droplet_cell_ontology_class.csv"))

write.csv(tiss@meta.data[,c('channel','cell_ontology_class','cell_ontology_id')], file="Bladder_droplet_cell_ontology_class.csv")
```



############no analysis completed below
# Subset and iterate

We can repeat the above analysis on a subset of cells, defined using cluster IDs or some other metadata. This is a good way to drill down and find substructure.

## First subset

```{r}
# Subset data based on cluster id
subtiss <- SubsetData(object = tiss, ident.use = c(3), do.center = F, do.scale = F, cells.use = )

# To subset data based on cell_ontology_class or other metadata, you can explicitly pass cell names

cells.to.use = tiss@cell.names[which(tiss@meta.data$mouse.sex == 'F')]
subtiss <- SubsetData(object = tiss, cells.use = cells.to.use, do.center = F, do.scale = F)
```

```{r}
subtiss <- NormalizeData(object = subtiss)
subtiss <- ScaleData(object = subtiss, vars.to.regress = c("nUMI", "percent.ribo","Rn45s"))
```

Run Principal Component Analysis.

```{r}
subtiss <- FindVariableGenes(object = subtiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.8)
subtiss <- RunPCA(object = subtiss, pcs.compute = 20, weight.by.var = F)
subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)
```

```{r}
# If this fails for your subset, it may be that cells.use is more cells than you have left! Try reducing it.
PCHeatmap(object = subtiss, pc.use = 1:3, cells.use = 250, do.balanced = TRUE, label.columns = FALSE, num.genes = 12)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = subtiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
sub.n.pcs = 5
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

```{r}
# Set resolution 
sub.res.used <- 1

subtiss <- FindClusters(object = subtiss, reduction.type = "pca", dims.use = 1:sub.n.pcs, 
    resolution = sub.res.used, ,print.output = 0, save.SNN = TRUE)
```

To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
subtiss <- RunTSNE(object = subtiss, dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=20)
```

```{r}
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = subtiss, do.label = T)
```

```{r}
subtiss.markers <- FindAllMarkers(object = subtiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

```{r}
subtiss.markers %>% group_by(cluster) %>% top_n(6, avg_diff)
```

Check expression of genes of interset.
```{r}
genes_to_check = c('Alb', 'Cyp2f2', 'Cyp2e1', 'Hamp', 'Glul', 'Ass1', 'Axin2', 'Igfbp2')

FeaturePlot(subtiss, genes_to_check, pt.size = 1)
```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(subtiss, genes_to_check, plot.legend = T)
```

How big are the clusters?
```{r}
table(subtiss@ident)
```

## Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = subtiss, do.return = TRUE, group.by = "channel")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(subtiss@ident), as.character(subtiss@meta.data$channel))
```

# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                    paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```

```{r}
# To reload a saved object
# filename = here('00_data_ingest', '04_tissue_robj_generated', 
#                      paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
# load(file=filename)
```


# Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', '03_tissue_cell_ontology_class_csv', 
                    paste0(tissue_of_interest, "_droplet_cell_ontology_class.csv"))
write.csv(tiss@meta.data[,c('channel','cell_ontology_class','cell_ontology_id')], file=filename)
```

